Big Data Analytics for Customer Success

In this special guest feature, Craig Soules, Founder & CEO of Natero, discusses how customer analytics helps customer success teams identify improvements to their product, process, and marketing and how by moving beyond anecdotal evidence, customer analytics can rapidly accelerate the pace of improvement for SaaS businesses. Craig Soules is Founder & CEO of Silicon Valley-based Natero, a leading Customer Success Platform for B2B SaaS companies. Natero, founded in 2012, helps companies place actionable data directly in the hands of customer success teams through simple and intuitive interfaces. Prior to forming Natero, Craig was Principal Researcher at HP Labs, where he led the team that productized Lazybase, a scalable database for processing a mix of high-speed, high-volume event data and more traditional tabular data. Natero integrates with over 35 popular sources of customer data, including Salesforce. Natero introduced a Customer Success Management platform that provides a full suite of predictive analytics based on state-of-the-art machine learning technology, combined with advanced customer analytics to help SaaS companies optimize their products and services. Craig holds a Ph.D. in Computer Science from Carnegie Mellon University.

Every SaaS company wants successful customers, but Customer Success requires a clear understanding of expected value. Analytical techniques can provide such insights through thoughtful examination of customer data.

There are three common types of business analytics:

Descriptive analytics, which provides insight from historical data.

Predictive analytics, which predicts trends and behavior patterns.

Prescriptive analytics, which focuses on the best course of action for a given situation.

Customer Success can benefit from all of three.

Customer Success teams are increasingly turning to technology for predictive analytics, which helps them identify customers who are at-risk or those ready to convert or buy more. That’s a tremendously valuable capability that lets SaaS businesses proactively engage customers.

Equally valuable is the insight that comes from applying descriptive analytics to customer data within a SaaS organization. Beyond predicting customer behavior, Customer Success teams can identify ways for their company to improve their products, processes, and marketing – all of which contribute to engaged and successful customers.

Customer Data

Customer data often resides in a variety of systems within a business. These typically include:

CRM: industry, size, stage, signup date, product/plan tier, etc.

Marketing: lead source, campaign responsiveness, CAC, etc.

Support: problem ticket frequency, severity, resolution time, etc.

Billing: MRR, invoice history, payment history, renewal date, etc.

Also, many businesses capture:

Product Usage – Activity level, Feature usage, User adoption

Health score

CSM interaction history

CSM perspective

Survey data

NPS data

Combining account details with customer behavior and feedback provides a complete picture of the customer. Customer analytics leverages this rich data to identify where businesses can make improvements in their products, processes, and marketing. Let’s break down some of the popular analytical techniques:

Aggregation and Segmentation

Aggregation and segmentation are a good place to start for any analysis. They give you a rapid understanding of an account, and allow you to test assumptions. Aggregation takes a set of data and pulls it into a single value. For example, the number of unique active users over the last 30 days, or the total number of times a given product feature was used in the last week. When viewed over time, aggregated metrics show a trend of changes in customer behavior.

Segmentation lets you define a group of customers by their characteristics. For example, accounts with MRR over $2,000, or users that have used a certain product feature in the last week. You can then view aggregated metrics over just the customer segments you are interested in, allowing you to compare and contrast these segments.

Understanding the factors that affect success within different segments can allow you to create action plans that more directly target those customers’ issues. You can even compare an individual at-risk account to similar accounts (e.g. same industry, stage, size) that are doing well to develop recovery strategies.

Aggregation and segmentation can also help businesses understand the profile of successful accounts. Focusing acquisition programs on customers who are more likely to succeed with your solution can accelerate the growth of your company.

The challenge of using aggregations and segmentation is the vast amount of data there is to explore. Over time, Customer Success teams will develop intuitions that will speed this process, while other advanced analytics will help to gain additional insights.

Cohorts

Cohort analysis segments customers by a point in time, rather than by certain characteristics.

A common use of cohort analysis places customers into a group, or cohort, based on the week they signed up. You can then track those cohorts over time to monitor changes in customer retention, health score, activity level, etc. As you improve your product week-to-week, you hope to see that customers in later cohorts show improvements in those metrics.

However, cohorts can be used to track a wide array of behaviors over time. You can place customers into groups based on when they first performed an action and track them after that initial action. This type of analysis shows you the “stickiness” of product features. Those features that customers continue using are likely delivering more value than those that customers use infrequently or stop using.

Cohort analysis can be combined with segmentation to refine your understanding of customer behavior. Using the feature stickiness example, you can segment customers by industry, stage, size, high-touch/low-touch, etc. to see if certain features are stickier in some segments than others. This shows you which features to promote to different customer types, and gives you data to show your product and marketing teams how different customers value different product capabilities.

Funnels

Funnel analysis focuses on the number of customers that completed a series of steps and how long it took them to complete them. To use funnel analysis, you define a series of actions you expect customers to perform and track them through those steps.

A common funnel analysis involves tracking users through a product registration process. This may involve clicking a registration button, filling out a form, downloading client software, creating a password and logging in. By constructing a funnel to track these steps, you can see the percentage of customers that complete each step, how long it takes to complete each step, and where any significant drop-offs occur.

To get the most value from funnel analytics, start with the outcome you want to track, e.g. how many people complete the registration process. Then work your way backward through the process to identify the key steps of completing it. Those steps then define your funnel. If there are multiple paths, it’s usually worth defining and tracking multiple funnels. Once you identify drop-off points in your funnels, it should give you some intuition as to what the problem is. If the problem remains vague, add additional steps to the funnel around that point to refine and pinpoint the problem area.

Funnel analysis is extremely useful for product teams to learn where they can make improvements in their solution. But it is also valuable to Customer Success teams who can act with foresight – if a particular part of the product is challenging, they can better guide their customers during onboarding or training.

Regressions

Regression analysis identifies the correlation between different metrics. For example, you could run a regression that checks the correlation between login frequency and revenue. The result could be a positive correlation (revenue goes up with increased login frequency), a negative correlation (revenue goes down with increased login frequency) or no correlation (there is no direct correlation between revenue and login frequency).

Regression is a helpful way to identify time trends from noisy data. By identifying the correlation between time and a metric, you can determine if that metric has been increasing or decreasing in a significant way, even if the data is visually noisy.

Resource Links:

Industry Perspectives

In this special guest feature, Brian D’alessandro, Director of Data Science at SparkBeyond, discusses how AI is a learning curve, and exploring opportunities within the technology further extends its potential to enable transformation and generate impact. It can shape workflows to drive efficiency and growth opportunities, while automating other workflows and create new business models. While AI empowers us with the ability to predict the future — we have the opportunity to change it. [READ MORE…]

Latest Video

White Papers

Darwin, a machine learning platform, accelerates data science at scale by automating the building and deployment of models. It provides a productive environment that empowers data scientist with a broad spectrum of experience to quickly prototype use cases and develop, tune, and implement machine learning applications in less time. Download the latest white paper from SparkCognition that compares how Darwin performs against other platforms in the market on the same datasets.